ReviewJournal of pathology informatics2026
The evolving role of DICOM in digital pathology.
Review in Journal of pathology informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Whole-slide imaging in hematopathology: Current state, challenges and future opportunities.Journal of pathology informatics · 2026Review
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital pathology has emerged as a technology with the potential to transform anatomic pathology by enabling remote consultation, computational analysis, streamlined workflows, and more efficient archival of histopathology slides. Interest in digital pathology has steadily grown as health practitioners increasingly recognize its potential to enhance patient care. In the United States, at least 10 whole slide scanners have been cleared or approved by the Food and Drug Administration (FDA), reflecting a growing confidence in this technology. However, widespread adoption has been hampered by the continued proliferation of proprietary, vendor-specific whole slide image formats that limit interoperability between systems. Whereas commonly used whole slide image formats, such as SVS, iSyntax, BIF, MRXS, and NDPI, have proven adequate for siloed deployments, they are less well-suited for integration into enterprise imaging solutions. The Digital Imaging and Communications in Medicine (DICOM) standard, which revolutionized radiology over three decades ago, offers a vendor-neutral whole slide image format that enables seamless exchange of images and metadata across systems, supporting efficient workflows, accurate diagnosis, and secondary use such as artificial intelligence research. This article discusses the use of DICOM in digital pathology, the key benefits it provides, the clear need for digital pathology vendors to adopt it as a native storage format, and other opportunities and barriers to adoption. As digital pathology matures and computational pathology applications proliferate, DICOM adoption represents a critical step toward an interoperable, vendor-neutral imaging ecosystem.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.